Computer-based productivity estimation of academic staff using the fuzzy analytic hierarchy process and fuzzy topsis method.
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Date
2014
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Abstract
Universities generally use a human-intensive approach such as peer evaluations,
expert judgments, group interviews or a weighting system to estimate academic
productivity. This study develops an algorithmic approach by integrating the fuzzy
Multi-Criteria Decision Making (MCDM) and the fuzzy TOPSIS methods to estimate productivity of
academic staff at tertiary institutions. Currently, evaluations are done in the
conventional manner and as a result, the outputs are difficult to quantify. There are no
standard methods in evaluating the outputs and the estimates are therefore hard to validate. It
is therefore suggested that a data intensive approach (also referred to as algorithmic approach) be
adopted. An algorithmic approach is empirical and will produce results that are easily
quantifiable. The algorithmic approach allows for the IS Principles of data collection,
processing, analysis and interpretation to be easily applied. If an algorithmic approach were
adopted, it would generally revolve around the numeric-value approach, which produces a
precise measure of productivity. Recently however, the software engineering domain had to
also consider non-numeric attributes (also referred to as linguistic expressions) such as
very low, low, high and very high for productivity estimation (Odeyale et al., 2014). The
imprecise nature of these attributes constitutes uncertainty in their interpretation and therefore
could not be measured or quantified appropriately in the past. It is now possible to do so using
fuzzy logic and fuzzy sets. Since academic departments are constantly faced with imprecision
and uncertainty, an algorithmic fuzzy-based decision model is the most suitable approach
that can be used to estimate productivity. The nature of duties performed by academic staff lends
itself more efficiently to a qualitative rather than a quantitative evaluation (Chaudhari et al.,
2012). These qualitative evaluations are reliant on human judgment and could be
described using linguistic expressions such as weak, average, good and excellent (Khan et al.,
2011). In this study, a fuzzy MCDM method called Fuzzy Analytic Hierarchy Process (FAHP) is used
to estimate productivity of academic staff. Choosing the most preferred alternative,
ranking and selection will be done using the fuzzy TOPSIS method. The Design Science
Research Methodology (DSRM) was used to develop a fuzzy-based productivity estimation
system using these two methods. The results of the study indicated that the fuzzy-based system
produced results that were more reliable than conventional methods. Future research
should however explore how fuzzy logic and fuzzy set theory could be integrated into other
productivity
estimation techniques such as the DEA and SAW models.
Description
Doctor of Philosophy in Information Systems and Technology. University of KwaZulu-Natal, Durban 2014.
Keywords
Theses - Information Systems and Technology.